Family medicine residents' training in, knowledge about, and perceptions of digital rectal examination.
Bibliographic record
Abstract
OBJECTIVE: To evaluate family medicine residents' training in, knowledge about, and perceptions of digital rectal examination (DRE). DESIGN: Descriptive study, using an online survey that was available in French and English. SETTING: Quebec. PARTICIPANTS: A total of 217 residents enrolled in a family medicine program. MAIN OUTCOME MEASURES: Residents' demographic characteristics; the DRE teaching they received throughout their medical training; their reasons for omitting DRE; their recognition of DRE indications (strong vs weak) and application of DRE for 10 anorectal complaints; and their perceptions of the overall quality of the DRE training they received. RESULTS: Of the 879 residents contacted, 217 (25%) responded to the survey. Throughout their training, one-third of respondents did not receive any supervision for or feedback on DRE technique. Seventy-one percent of respondents expressed their inability to identify the nature of abnormal examination findings at least once during their training. The most frequently reported reasons to omit DRE were patient refusal, inadequate setting, and lack of time. CONCLUSION: Most of the residents in this study had omitted DRE at least once in their clinical work despite recognizing its importance. There was discordance between recognition of a complaint requiring DRE and execution of this technique in a clinical setting. Family medicine education programs and continuing medical education committees should consider including DRE training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".